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<table width="100%" summary="page for rwm"><tr><td>rwm</td><td align="right">R Documentation</td></tr></table>

<h2>rwm</h2>

<h3>Description</h3>


<p>German health registry for the years 1984-1988. Health information for 
years prior to health reform. 
</p>


<h3>Usage</h3>

<pre>data(rwm)</pre>


<h3>Format</h3>


<p>A data frame with 27,326 observations on the following 4 variables.
</p>

<dl>
<dt><code>docvis</code></dt><dd><p>number of visits to doctor during year (0-121)</p>
</dd>
<dt><code>age</code></dt><dd><p>age: 25-64</p>
</dd>
<dt><code>educ</code></dt><dd><p>years of formal education (7-18)</p>
</dd>
<dt><code>hhninc</code></dt><dd><p>household yearly income in DM/1000)</p>
</dd>
</dl>



<h3>Details</h3>


<p>rwm is saved as a data frame.
Count models typically use docvis as response variable. 0 counts are included
</p>


<h3>Source</h3>


<p>German Health Reform Registry, years pre-reform 1984-1988, 
From Hilbe and Greene (2008)
</p>


<h3>References</h3>


<p>Hilbe, Joseph M (2011), Negative Binomial Regression, Cambridge University Press
Hilbe, J.M. and W.H. Greene (2008), &quot;Count Response Regression Models&quot;, in Rao, CR, 
JP Miller and DC Rao (eds), Handbook of Statistics 27: Epidemiology and Medical 
Statistics, Amsterdam: Elsevier.  pp. 210-252.
</p>


<h3>Examples</h3>

<pre>
data(rwm)
glmrwp &lt;- glm(docvis ~ age + educ + hhninc, family=poisson, data=rwm)
summary(glmrwp)
exp(coef(glmrwp))
library(MASS)
glmrwnb &lt;- glm.nb(docvis ~ age + educ + hhninc, data=rwm)
summary(glmrwnb)
exp(coef(glmrwnb))
</pre>


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